In perceptual image encryption [1]}, [2]}, [3]}, the shuffling of the pixels in the image blocks can destroy the local spatial structure of the local areas and prevent the network from extracting useful features. This shows that the intra-block shuffling plays a similar role as occluding local areas. Since occlusion of random areas is a data enhancement method to improve the robustness of the network [4]}, we propose that intra-block shuffling can achieve the same effect. Moreover, shuffling retains the distribution information of the local area and the neural network can extract local statistical features and other local structure-independent features. Therefore, we designed a transformation method to achieve the goal as follows.
